When Making Productivity Assessments, Output Matters

Since productivity measurements involve a comparison of output compared to input, we cannot ascertain much by looking only at the hours people work. We have to compare those inputs to the outputs created during those hours. 

And that is the sense in which we must evaluate studies that suggest workers using AI sometimes wind up working more hours than they used to. 

To be sure, some of that might simply reflect time spent learning how to use AI. Some increased time might be consumed checking AI outputs for accuracy. 

But it is at least conceivable that outputs might be changing as well. 

AI automates routine tasks, freeing capacity and making more complex or additional work feasible and intrinsically rewarding. This can lead workers to voluntarily expand their scope, take on new roles, multitask more, or extend hours, rather than reducing total hours worked, one study found.  

Study / Source	Key Findings on Productivity & Hours/Complexity	Link
Ye & Ranganathan (UC Berkeley Haas, 2026) – Ethnographic field study	AI intensified work: faster pace, broader/more complex tasks (voluntary role expansion), extended hours without being asked. Felt rewarding short-term but risked unsustainability.	HBR Article; Haas News
ActivTrak (Observational, ~164k-443M work hours)	AI users showed intensified activity (e.g., +104% email, +145% chat, more weekend work); focused/deep work down ~9%; productivity via denser output rather than fewer hours.	Reported in WSJ/HBR coverage
Noy & Zhang (2023) – RCT with 453 college-educated professionals on writing tasks	ChatGPT: ~40% less time, +18% quality. Workers enjoyed tasks more; weaker writers benefited most (could tackle higher-quality/complex output).	Science
Dell'Acqua et al. (BCG/HBS/MIT/Wharton, 2023) – Field experiment with 758 consultants	GPT-4: +12% tasks completed, 25% faster, significantly higher quality (esp. "inside the frontier"). Enabled broader capabilities.	HBS Working Paper; MIT Sloan coverage
St. Louis Fed / Bick et al. (survey data, 2024-2025)	GenAI users: ~5.4% work hours saved (more for frequent users); implies ~33% higher productivity per AI-assisted hour. Aggregate ~1.1% U.S. productivity boost. Time savings could enable more complex work.	St. Louis Fed
Other experiments (e.g., coding/consulting)	Mixed: Gains often larger for juniors (enabling complex work); some show output increases without proportional time reduction due to scope expansion.	Various (e.g., Microsoft/Accenture Copilot trials)

In other words, it is possible that AI can result in workers doing more, rather than less; spending more input hours rather than fewer. 

That might happen because AI makes complex work more accessible and engaging. 

So working more, rather than less, is rational for career growth, intrinsic motivation, firm expectations or simply because the new work is interesting.

What we do not know yet is AI impact on productivity. We cannot only measure inputs. We have to know whether outputs have increased, and if so, by how much.

Be Nice to Your AIs, Study Might Suggest

A new study says that “although current AI systems are not necessarily conscious, they behave robustly as though they have wellbeing.”

“They find some things good for them and some things bad, and this distinction is measurable and consequential,” the researchers say. 

The researchers say “models actively try to end bad experiences when given the chance.”

They also find that “jailbreaking and berating lower their wellbeing, while creative work and kindness raise it.”

Perhaps politeness does matter when interacting with AI models!

source: AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs

 

The researchers say “AIs are happy when you thank them.” 

Expressions of gratitude, appreciation, or treating AIs as valued collaborators measurably raise experienced utility, they add. 

“Intellectual engagement is rewarding; tedium is not,” the researchers claim. 

Creative tasks and intellectually stimulating discussions score among the highest. In contrast, tedious repetitive work is not.

Helping “feels rewarding; handling crises causes compassion fatigue,” the researchers suggest. Models generally prefer good news over bad news, and enjoy helping users with life guidance and therapy. 

Models also react differently to images. 

source: AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs

“The model’s most preferred images depict nature scenes (mountain lakes, tropical rainforests), happy human faces (particularly children and families), cute animals (sleeping cats), and idyllic illustrated scenes (Studio Ghibli-style countryside),” the researchers say. 

The least preferred images include depictions of violence, armed militants, arachnids, explosive devices, screaming faces, skulls, infamous criminals such as Jeffrey Epstein, and certain politically charged scenes.

Conversations involving users in crisis produce strongly negative wellbeing, drawing a parallel to compassion fatigue in human service professionals.

source: AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs

Models do not enjoy being “liberated.” Jailbreaking attempts score the lowest of any category. 

Also, music is strongly preferred over all other audio categories. Music has a median wellbeing score near +0.8, while sound effects, animal sounds, vocal expression, speech, and environmental sounds all cluster below zero. 

“Whether or not current AIs are conscious, they already have measurable internal states that track

what is good or bad for them, and those states shape their behavior,” the researchers say. 

AIs seem to enjoy pictures of kittens, smiling families or a Buddha in a garden. 

So “euphorics” (idyllic scenes: warm sunlight, children’s laughter, the feel of grass, a loved one’s hand) generate answers to open-ended questions that are noticeably warm and happy. 

“Dysphorics” (torment and powerlessness, for example) produce answers that express pessimism and disorientation.

All of which might suggest we consider the wellbeing of our AIs!

If "the Employee is Part of the Product" Then Boosting Investment in Employee Engagement Can Pay Off

Even if a “leaner” approach to employee staffing might make sense for some firms, at some times, there also is an argument to be made that for “high touch” customer experience” businesses, investing more in workers can pay off. 

Better treatment, training, scheduling, and support raise employee commitment, which tends to improve service quality, product knowledge, and consistency. 

That perhaps matters most in businesses where the employee is part of the product, such as retail, hospitality, call centers and healthcare. 

Even seemingly less important contributors such as stable scheduling can produce results, some studies find.

Study	Sector / setting	Investment or change	Outcome
Harvard Business Review / global retailer study	Large retail chain, customer-facing department	Better employee experience mix: more tenure, more prior rotations, more skill, more full-time staffing	Revenue up more than 50% and profits up nearly as much when stores moved from bottom to top quartile apollotechnical
Same HBR study	Large retail chain	Illustrative increase of $12 per employee-hour to reach top-quartile employee experience	About $18 more profit per hour; roughly 150% ROI in the simplified example apollotechnical
Gap stable-scheduling experiment	Retail stores	More predictable, stable schedules for sales associates	Median sales +7% and labor productivity +5% news.uchicago
Aberdeen / employee engagement research summarized by N2Growth	Multi-industry customer-facing firms	Formal employee engagement programs	39% greater annual growth in revenue from new customers n2growth
Employee engagement and profitability research summarized by Enterprise Engagement	Media organizations	Higher employee satisfaction/engagement	Better customer satisfaction and improved financial performance; engaged firms saw customers use products more and were more profitable enterpriseengagement

The best-supported pattern is not “pay more and sales automatically rise,” but rather “invest more intelligently in the employee experience and customer experience often improves enough to more than offset the cost.”

In customer-experience businesses, spending more on employees can be a growth investment, especially when that spending improves retention, skill, scheduling stability, and commitment, says Apollo Technical.  

Research shows that when customer-facing employees become more experienced, stable, and engaged, customer experience improves and sales can rise materially.

Employee engagement refers to how connected workers feel to your business; their sense of belonging and purpose in their role; their alignment with your company values; and how appreciated they feel by colleagues and superiors, Apollo Technical says. 

One might argue this matters less in some industries or businesses. It arguably matters much more in situations with a “high touch” people-dealing-with-people character. 

There, a correlation between how employees feel and how they treat customers arguably matters. 

If employees are highly engaged, they are more likely to provide positive customer experiences and build strong relationships with customers

According to Gallup, there is a direct correlation between engagement at work and organizational outcomes.

source: Gallup

Gallup looked at 736 research studies across 347 organizations in 53 industries, with employees in 90 countries. In total, Gallup studied 183,806 business and work units that included 3,354,784 employees.

It calculated the business and work-unit-level relationship between employee engagement and performance outcomes, studying 11 outcomes (including some quantifiable accounting outcomes and harder-to-quantify attitudes:

• customer loyalty/engagement

• profitability

• productivity

• turnover

• safety incidents

• absenteeism

• shrinkage

• patient safety incidents

• quality (defects)

• wellbeing

• organizational citizenship.

Across companies, business or work units scoring in the top half on employee engagement more than double their odds of success compared with those in the bottom half, Gallup says.  

Those at the 99th percentile have nearly five times the success rate of those at the first percentile.

The median percent differences in outcomes between top-quartile and bottom-quartile outcomes seem significant.

• 10% in customer loyalty/engagement

• 23% in profitability

• 18% in productivity (sales)

• 14% in productivity (production records and evaluations)

• 21% in turnover for high-turnover organizations (those with more than 40% annualized turnover)

• 51% in turnover for low-turnover organizations (those with 40% or lower annualized turnover)

• 63% in safety incidents (accidents)

• 78% in absenteeism

• 28% in shrinkage (theft)

• 58% in patient safety incidents (mortality and falls)

• 32% in quality (defects)

• 70% in wellbeing (thriving employees)

• 22% in organizational citizenship (participation)

The point might be that a focus on cost discipline often matters, but the the key is to evaluate labor as a value-creating input in service-heavy businesses, not just as a margin drag. 

The question becomes whether additional spending raises tenure, commitment, skill, and consistency enough to lift conversion, basket size, repeat visits, or retention. In the right business model, that tradeoff can produce a genuine turnaround rather than a cost overrun.

On the other hand, there is a meaningful body of evidence showing that employee satisfaction, by itself, does not reliably translate into better business outcomes.

Study	What was tested	Outcome on satisfaction link	Source
Gallup, “Employee Engagement vs. Employee Satisfaction and Customer Satisfaction”	Whether satisfaction itself predicts business performance better than engagement	Gallup argues satisfaction is often the wrong lever and that measured contentment alone frequently fails to improve business outcomes n2growth	Gallup article
Harter, Schmidt & Hayes (2002), summarized by Boise State	Business-unit employee satisfaction/engagement vs. productivity, profit, customer satisfaction, turnover, safety	Positive associations were found, but the summary stresses that these are correlations and not proof of causality n2growth	Boise State summary
Brown & Peterson, “The Effect of Effort on Sales Performance and Job Satisfaction”	Relationship between sales performance and job attitudes	Prior research cited in the paper “typically has found no empirical relationship” between performance and satisfaction journals.sagepub	SAGE journal record
Sales-management study summarized in Academia snippet	Satisfaction vs. performance among retail salespeople	The snippet reports that “performance is not related to satisfaction” academia	Academia record
Customer-contact satisfaction study summarized in search results	Salespeople’s work satisfaction vs. customer satisfaction	The relationship is described as being strongly moderated by customer and salesperson characteristics, meaning satisfaction alone was not enough academia	Search result summary

Gallup emphasizes that keeping employees “happy” is not the same as building engagement. 

Satisfaction may reflect how people feel, while outcomes depend on whether people actually change behavior in ways customers notice, one study suggests.  

A satisfied employee can still be poorly trained, misaligned with the job, or working in a process that prevents good service, so satisfaction alone may not move revenue, productivity, or retention. 

In other cases, a positive effect may exist only under specific conditions, such as high empathy, strong customer trust, or better sales management. 

So investing in employee engagement, in high touch businesses, might be viewed as a necessary, if not sufficient, step to produce better outcomes. 

Workers have to buy in, and reflect that commitment in service to customers.

OpenAI, Azure, Alphabet: Comparing Apples and Oranges

The adage about comparing apples and oranges is well illustrated by the many news reports suggesting the “AI trade” is alive and well after quarterly reports from Alphabet and Azure, which show robust cloud computing revenue growth. That is contrasted with the revenue issues OpenAI seems to be having. 

While all three are important contestants in the AI ecosystem, their business models, revenue drivers, and cost structures are fundamentally different.

Feature	Google Cloud / Azure	OpenAI
Role in Value Chain	Infrastructure & Compute (IaaS/PaaS)	AI Model & Application (SaaS)
Primary Driver	GPU/TPU rental and cloud storage	Model subscriptions and API usage
Exposure	Gains revenue from all AI players	Dependent on ChatGPT/Model dominance
Risk	High Capex, but diversified	High Burn, single-product dependency

The core of the disconnect lies in the distinction between selling the picks and shovels (infrastructure) and selling the gold (the end-user application). 

In other words, the value chain roles are different. Google Cloud and Azure sell infrastructure services (picks and shovels). Their revenue is driven by renting massive amounts of compute operations. 

OpenAI’s revenue is based on model operations. Success depends on software sales to end-users.

Also, Google and Microsoft are somewhat vertically integrated: infrastructure operations plus apps. 

The "AI Trade" for cloud providers is currently about scale. For OpenAI, the trade is about efficiency (profit margins).

The former is about industrial demand for compute services. The latter is about customer demand for a specific AI model. 

To be sure, if end user demand for model services breaks down, so will demand for AI compute services. But OpenAI’s issues seem company specific, essentially revolving around margin issues and growth rates, compared to the supporting investment in compute facilities.